Applied Scientist, Optimization & Logistics

Turn ambiguous operational problems into well-posed optimization, forecasting, or simulation tasks.

Build strong baselines and improve on them efficiently, adding complexity only when the value justifies it.

Develop solutions across operations research, optimization, and machine learning, choosing the right tool for the problem.

Run careful analysis and iterate toward decisions that improve real operational outcomes — cost per visit, clinician utilization, patient access, and visits completed.

Design offline evaluations, simulated backtests, and live experiments that predict real-world operational impact.

Find the gaps between a model’s assumptions and messy operational reality before they reach production.

Choose metrics suited to stochastic, constrained, and partially observed operational systems.

Interpret and communicate results effectively to cross-functional stakeholders.

Partner with Engineering to productionize optimization and decision systems reliably.

Work with operations partners and SMEs to validate assumptions and review where decisions break down.

Explain tradeoffs, uncertainty, and limitations clearly to product and leadership.

What you have done:

Strong foundations in operations research or optimization: modeling, algorithms, experimental design, and honest evaluation.

Strong Python and SQL, the standard optimization and ML libraries, and the ability to run your own experiments end to end.

Fluency with AI coding assistants (e.g., Claude Code, Cursor) in your day-to-day development workflow.

Ability to turn an ambiguous problem into a well-posed optimization or forecasting task, discover and analyze related literature, and adapt/apply those methods to our tasks.

Judgment about how uncertainty, constraints, and edge cases behave in real-world operational data.

Interest in operations collaboration and applied healthcare impact.

What gives you an edge:

MS or PhD in operations research, industrial engineering, computer science, applied math, statistics, machine learning, or a related quantitative field; exceptional applied experience can substitute.

Depth in a relevant area such as vehicle routing, scheduling, stochastic optimization, discrete-event simulation, queueing, or demand forecasting.

Experience shipping optimization or decision systems that reached production and had material real-world impact.

Hands-on experience with supply-and-demand matching in a marketplace, dispatch, or field-operations setting.

Fluency deciding when an exact optimization approach beats a heuristic or learned one, and vice versa.

Interview Process:

We aim to complete the interview process between 2–3 weeks. It will usually consist of:

Recruiter Screen (30 minutes)

Hiring Manager Introduction (30 minutes)

Hands-on-Keys Technical Assessment (1 hour)

Onsite Interview: Systems Design / Technical Case Study + Research Presentation + Behavioral Interview + Lunch with the Team (4 hours)

References

What we offer:

Meaningful pre-IPO equity

Medical, dental, and vision plans 100% paid for you and your dependents

Flexible PTO + 10 paid holidays per year

401(k) with match

16-week parental leave policy for birthing parent, 8 weeks for all other parents

HSA + FSA contributions

Life insurance, plus short and long-term disability coverage

Free daily lunch in-office

Annual learning stipend